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Journal of Integrative Agriculture  2017, Vol. 16 Issue (07): 1547-1557    DOI: 10.1016/S2095-3119(16)61497-1
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Automated detection and identification of white-backed planthoppers in paddy fields using image processing
YAO Qing1, CHEN Guo-te1, WANG Zheng1, ZHANG Chao1, YANG Bao-jun2, TANG Jian2
1 School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, P.R.China
2 State Key Laboratory of Rice Biology, China National Rice Research Institute, Hangzhou 310006, P.R.China
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Abstract      A survey of the population densities of rice planthoppers is important for forecasting decisions and efficient control. Traditional manual surveying of rice planthoppers is time-consuming, fatiguing, and subjective. A new three-layer detection method was proposed to detect and identify white-backed planthoppers (WBPHs, Sogatella furcifera (Horváth)) and their developmental stages using image processing. In the first two detection layers, we used an AdaBoost classifier that was trained on a histogram of oriented gradient (HOG) features and a support vector machine (SVM) classifier that was trained on Gabor and Local Binary Pattern (LBP) features to detect WBPHs and remove impurities. We achieved a detection rate of 85.6% and a false detection rate of 10.2%. In the third detection layer, a SVM classifier that was trained on the HOG features was used to identify the different developmental stages of the WBPHs, and we achieved an identification rate of 73.1%, a false identification rate of 23.3%, and a 5.6% false detection rate for the images without WBPHs. The proposed three-layer detection method is feasible and effective for the identification of different developmental stages of planthoppers on rice plants in paddy fields.
Keywords:  white-backed planthopper       developmental stage        automated detection and identification        image processing        histogram of oriented gradient features        gabor features        local binary pattern features  
Received: 01 September 2016   Accepted:

This work was financially supported by the National High Technology Research and Development Program of China (863 Program, 2013AA102402), the 521 Talent Project of Zhejiang Sci-Tech University, China, and the Key Research and Development Program of Zhejiang Province, China (2015C03023).

Corresponding Authors:  Correspondence TANG Jian, Tel: +86-571-63370331, E-mail:    
About author:  YAO Qing, Mobile: +86-13958015661, Tel: +86-571-86843324, E-mail:;

Cite this article: 

YAO Qing, CHEN Guo-te, WANG Zheng, ZHANG Chao1 YANG Bao-jun, TANG Jian. 2017. Automated detection and identification of white-backed planthoppers in paddy fields using image processing. Journal of Integrative Agriculture, 16(07): 1547-1557.

Chang C C, Lin C J. 2011. LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 3, 1–27.

Chen X N, Wu J C, Ma F. 2003. Study on the Prevention and Treatment of BPH. China Agriculture Press, Beijing. p. 73. (in Chinese)

Dalal N, Triggs B. 2005. Histograms of oriented gradients for human detection. Computer Vision and Pattern Recognition, 1, 886–893.

Ding J H, Xu G J, Lin G L. 1991. Agricultural Entomology. Science and Technology Press, Nanjing, China. p. 159. (in Chinese).

Freund Y, Schapire R E. 1997. A Decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 1, 119–139.

GB/T 15794-2009. 2009. The Standard on the Forecast and Survey of Rice Planthoppers. China National Standardization Management, Beijing. (in Chinese)

Hu Y D. 2014. Statistical Research On Crop Pests Based On Visual Technology. Zhejiang Sci-Tech University, China. (in Chinese)

Lee T S. 1996. Image representation using 2D gabor wavelets. IEEE Transactions on Pattern Analysis and Machine Intelligence, 18, 959–971.

Liu D Y, Ding W M, Chen K J. 2011. Automatic collecting device for insect image in field environment. Journal of Agricultural Machinery, 42, 1000–1298. (in Chinese)

Liu T, Chen W, Wu W, Sun C M, Guo W S, Zhu X K. 2016. Detection of aphids in wheat fields using a computer vision technique. Biosystems Engineering, 141, 82–93.

Ojala T, Pietikainen M, Maenpaa T. 1996. A comparative study of texture measures with based on feature distributions. Pattern Recognition, 29, 51–59.

Ojala T, Pietikainen M, Maenpaa T. 2002. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24, 971–987.

Park Y S, Han M W, Kim H Y, Uhm K B, Park C G, Lee J M, Chon T S. 2003. Density estimation of rice planthoppers using digital image processing algorithm. Korean Journal of Applied Entomology, 1, 57–63.

Schapire R E, Freund Y, Bartlett P, Lee W S. 1998. Boosting the margin: A new explanation for the effectiveness of voting methods. The Annals of Statistics, 26, 1651–1686.

Shan S G, Yang P, Chen X L, Gao W. 2005. Adaboost gabor fisher classifier for face recognition. In: International Conference on Analysis and Modeling of Faces and Gestures. Springer-Verlag, Berlin Heidelberg, Germany. pp. 279–292.

Viola P, Jones M J. 2004. Robust real-time face detection. International Journal of Computer Vision, 57, 137–154.

Vondrick C, Khosla A, Malisiewicz T, Torralba A. 2013. HOGgles: Visualizing object detection features. In: IEEE International Conference on Computer Vision. Sydney, Australia. pp. 1–8.

Wang R L, Lu M H, Han L Z, Yu F L, Chen F J. 2014. Methods and technologies for surveying and sampling the rice planthoppers, Nilaparvata lugens, Sogatella furcifera and Laodelphax striatellus. Chinese Journal of Applied Entomology, 51, 842–847. (in Chinese)

Yao Q, Xian D X, Liu Q J. 2014. Automated counting of rice planthoppers in paddy dields based on image processing. Journal of Integrative Agriculture, 13, 1736–1745.

Zou X G. 2013. Study on rice field identification technology based on machine vision. Ph D thesis, Nanjing Agricultural University. (in Chinese)

Zou X G, Ding W M. 2012. Design of processing system for agricultural pests with digital signal processor. Journal of Information & Computational Science, 15, 4575–4582. (in Chinese)
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